Forecasting Value-at-Risk of cryptocurrencies using the time-varying mixture-accelerating generalized autoregressive score model

Kunliang Jiang, Linhui Zeng, Jiashan Song and Yimeng Liu

Research in International Business and Finance, 2022, vol. 61, issue C

Abstract: We introduce the accelerating generalized autoregressive score (aGAS) technique into the Gaussian-Cauchy mixture model and propose a novel time-varying mixture (TVM)-aGAS model. The TVM-aGAS model is particularly suitable for processing the fat-tailed and extreme volatility characteristics of cryptocurrency returns. We then apply it to Value-at-Risk (VaR) forecasting of three cryptocurrencies, obtaining testing results that show our model possesses advantages in forecasting the density of daily cryptocurrency returns. Compared to other benchmarked models, the proposed model performs well in forecasting out-of-sample VaR. The findings underscore that our method is a useful and reliable alternative for forecasting VaR in cryptocurrencies.

Keywords: Time-varying mixture model; Accelerating generalized autoregressive score; Cryptocurrency markets; Risk management; Value-at-Risk (search for similar items in EconPapers)
JEL-codes: C22 C58 G32 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:riibaf:v:61:y:2022:i:c:s0275531922000228

DOI: 10.1016/j.ribaf.2022.101634

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